High-density, whole-transcriptome DNA microarrays are the method of choice for unbiased gene-expression profiling. These profiles have been found useful for the classification and diagnosis of disease, predicting patient response to therapy, exploring biological mechanisms, in classifying and elucidating the mechanisms-of-action of small molecules, and in identifying new therapeutics. van de Vijver et al., “A gene expression signature as a predictor of survival in breast cancer” N Engl J Med 347:1999-2009 (2002); Lamb et al., “A mechanism of cyclin D1 action encoded in the patterns of gene expression in human cancer” Cell 114:323-334 (2003); Glas et al., “Gene expression profiling in follicular lymphoma to assess clinical aggressiveness and to guide the choice of treatment” Blood 105:301-307 (2005); Burczynski et al., “Molecular classification of Crohn's disease and ulcerative colitis patients using transcriptional profiles in peripheral blood mononuclear cells” J Mol Diagn 8:51-61 (2006); Golub et al., “Molecular classification of cancer: class discovery and class prediction by gene expression monitoring” Science 286:531 (1999); Ramaswamy et al., “Multiclass cancer diagnosis using tumor gene expression signatures” Proc Natl Acad Sci 98: 15149 (2001); Lamb et al., “The Connectivity Map: using gene-expression signatures to connect small molecules, genes and disease” Science 313:1929 (2006). However, the overall success and wide-spread use of these methods is severely limited by the high cost and low throughput of existing transcriptome-analysis technologies. For example, using gene-expression profiling to screen for small molecules with desirable biological effects is practical only if one could analyze thousands of compounds per day at a cost dramatically below that of conventional microarrays.
What is needed in the art is a simple, flexible, cost-effective, and high-throughput transcriptome-wide gene-expression profiling solution that would allow for the analysis of many thousands of tissue specimens and cellular states induced by external perturbations. This would greatly accelerate the rate of discovery of medically-relevant connections encoded therein. Methods have been developed to rapidly assay the expression of small numbers of transcripts in large number of samples; for example, Peck et al., “A method for high-throughput gene expression signature analysis” Genome Biol 7:R61 (2006). If transcripts that faithfully predict the expression levels of other transcripts could be identified, it is conceivable that the measurement of a set of such ‘landmark’ transcripts using such moderate-multiplex assay methods could, in concert with an algorithm that calculates the levels of the non-landmark transcripts from those measurements, provide the full-transcriptome gene-expression analysis solution sought.
Citation or identification of any document in this application is not an admission that such document is available as prior art to the present invention.